Evidence map›Paper›PMID 39085518›Full record

ArticleLung2024

Longitudinal Cough Frequency Monitoring in Persistent Coughers: Daily Variability and Predictability.

Kian Fan Chung, Carlos Chaccour, Lola Jover, Mindaugas Galvosas, Woo-Jung Song, Matthew Rudd, Peter Small

Registry-linked trialAbstract read
In one paragraph

Article in Lung, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07212439 (Continuous Cough Monitoring for Early Detection of COPD Exacerbations Using Hyfe), which is not on this map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07212439 recruitingnot on this mapstarted 2025, after this paper: background citation

Continuous Cough Monitoring for Early Detection of COPD Exacerbations Using Hyfe

Typeobservational_patient_registrySponsorClinica Universidad de Navarra, Universidad de NavarraRan2025 to 2026Enrolled50ConditionsChronic Obstructive Pulmonary Disease (COPD)
3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026
    Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Converting a Cough Counter into a Cough Monitor: A Way Forward?Medical sciences (Basel, Switzerland) · 2026
    Review
  9. Observational
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Kian Fan ChungNational Heart and Lung Institute, Imperial College London, Dovehouse St, London, SW3 6LY, UK. f.chung@imperial.ac.uk.
Carlos ChaccourISGlobal, Barcelona Institute for Global Health, Barcelona, Spain.
Lola JoverHyfe Inc., Wilmington, DE, USA.
Mindaugas GalvosasHyfe Inc., Wilmington, DE, USA.
Woo-Jung SongDepartment of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Matthew RuddHyfe Inc., Wilmington, DE, USA.
Peter SmallHyfe Inc., Wilmington, DE, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe determined the cough counts and their variability in subjects with persistent cough for 30 days.

methodsThe Hyfe cough tracker app uses the mobile phone microphone to monitor sounds and recognizes cough with artificial intelligence-enabled algorithms. We analyzed the daily cough counts including the daily predictability rates of 97 individuals who monitored their coughs over 30 days and had a daily cough rate of at least 5 coughs per hour.

resultsThe mean (median) daily cough rates varied from 6.5 to 182 (6.2 to 160) coughs per hour, with standard deviations (interquartile ranges) varying from 0.99 to 124 (1.30 to 207) coughs per hour among all subjects. There was a positive association between cough rate and variability, as subjects with higher mean cough rates (OLS) have larger standard deviations. The accuracy of any given day for predicting all 30 days is the One Day Predictability for that day, defined as the percentage of days when cough frequencies fall within that day's 95% confidence interval. Overall Predictability was the mean of the 30-One Day Predictability percentages and ranged from 95% (best predictability) to 30% (least predictability).

conclusionThere is substantial within-day and day-to-day variability for each subject with persistent cough recorded over 30 days. If confirmed in future studies, the clinical significance and the impact on the use of cough counts as a primary end-point of cough interventions of this variability need to be assessed.

Indexed as

CoughAdultAgedAlgorithmsArtificial IntelligenceFemaleHumansLongitudinal StudiesMaleMiddle AgedMobile ApplicationsPredictive Value of TestsTime FactorsArtificial intelligenceChronic coughCough applicationsCough frequencyPersistent cough

Identifiers

PMID39085518
PMCPMC11427503

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.